Comparing Dataset A and Dataset B

Dataset statistics

	Dataset A	Dataset B
Number of variables	12	12
Number of observations	446	446
Missing cells	426	432
Missing cells (%)	8.0%	8.1%
Duplicate rows	0	0
Duplicate rows (%)	0.0%	0.0%
Total size in memory	45.3 KiB	45.3 KiB
Average record size in memory	104.0 B	104.0 B

Variable types

	Dataset A	Dataset B
Numeric	5	5
Categorical	4	4
Text	3	3

Alerts

Dataset A	Dataset B
`Sex` is highly overall correlated with `Survived`	`Sex` is highly overall correlated with `Survived`	High Correlation
`Survived` is highly overall correlated with `Sex`	`Survived` is highly overall correlated with `Sex`	High Correlation
`Age` has 84 (18.8%) missing values	`Age` has 88 (19.7%) missing values	Missing
`Cabin` has 341 (76.5%) missing values	`Cabin` has 342 (76.7%) missing values	Missing
`PassengerId` has unique values	`PassengerId` has unique values	Unique
`Name` has unique values	`Name` has unique values	Unique
`SibSp` has 294 (65.9%) zeros	`SibSp` has 306 (68.6%) zeros	Zeros
`Parch` has 334 (74.9%) zeros	`Parch` has 348 (78.0%) zeros	Zeros
`Fare` has 6 (1.3%) zeros	`Fare` has 9 (2.0%) zeros	Zeros

Reproduction

	Dataset A	Dataset B
Analysis started	2023-11-22 10:41:21.993689	2023-11-22 10:41:26.822515
Analysis finished	2023-11-22 10:41:26.821441	2023-11-22 10:41:30.213952
Duration	4.83 seconds	3.39 seconds
Software version	ydata-profiling v0.0.dev0	ydata-profiling v0.0.dev0
Download configuration	config.json	config.json

PassengerId
Real number (ℝ)

	Dataset A	Dataset B
Distinct	446	446
Distinct (%)	100.0%	100.0%
Missing	0	0
Missing (%)	0.0%	0.0%
Infinite	0	0
Infinite (%)	0.0%	0.0%
Mean	457.05605	455.97085

	Dataset A	Dataset B
Minimum	1	1
Maximum	891	890
Zeros	0	0
Zeros (%)	0.0%	0.0%
Negative	0	0
Negative (%)	0.0%	0.0%
Memory size	7.0 KiB	7.0 KiB

Quantile statistics

	Dataset A	Dataset B
Minimum	1	1
5-th percentile	40.5	48.75
Q1	237	230.25
median	461.5	458
Q3	686.75	681.75
95-th percentile	846.75	855.75
Maximum	891	890
Range	890	889
Interquartile range (IQR)	449.75	451.5

Descriptive statistics

	Dataset A	Dataset B
Standard deviation	259.63879	260.86626
Coefficient of variation (CV)	0.56806771	0.5721117
Kurtosis	-1.1973026	-1.23997
Mean	457.05605	455.97085
Median Absolute Deviation (MAD)	225.5	226.5
Skewness	-0.092729601	-0.030955301
Sum	203847	203363
Variance	67412.3	68051.206
Monotonicity	Not monotonic	Not monotonic

Histogram with fixed size bins (bins=50)

Value	Count	Frequency (%)
191	1	0.2%
527	1	0.2%
83	1	0.2%
256	1	0.2%
167	1	0.2%
428	1	0.2%
687	1	0.2%
610	1	0.2%
28	1	0.2%
327	1	0.2%
Other values (436)	436	97.8%

Value	Count	Frequency (%)
106	1	0.2%
708	1	0.2%
689	1	0.2%
409	1	0.2%
171	1	0.2%
450	1	0.2%
269	1	0.2%
498	1	0.2%
878	1	0.2%
545	1	0.2%
Other values (436)	436	97.8%

Minimum 10 values
Maximum 10 values

Value	Count	Frequency (%)
1	1	0.2%
2	1	0.2%
4	1	0.2%
6	1	0.2%
7	1	0.2%
8	1	0.2%
9	1	0.2%
12	1	0.2%
14	1	0.2%
15	1	0.2%

Value	Count	Frequency (%)
1	1	0.2%
3	1	0.2%
4	1	0.2%
5	1	0.2%
7	1	0.2%
8	1	0.2%
9	1	0.2%
14	1	0.2%
17	1	0.2%
20	1	0.2%

Value	Count	Frequency (%)
1	1	0.2%
3	1	0.2%
4	1	0.2%
5	1	0.2%
7	1	0.2%
8	1	0.2%
9	1	0.2%
14	1	0.2%
17	1	0.2%
20	1	0.2%

Value	Count	Frequency (%)
1	1	0.2%
2	1	0.2%
4	1	0.2%
6	1	0.2%
7	1	0.2%
8	1	0.2%
9	1	0.2%
12	1	0.2%
14	1	0.2%
15	1	0.2%

Survived
Categorical

	Dataset A	Dataset B
Distinct	2	2
Distinct (%)	0.4%	0.4%
Missing	0	0
Missing (%)	0.0%	0.0%
Memory size	7.0 KiB	7.0 KiB

0	287
1	159

0	275
1	171

Length

	Dataset A	Dataset B
Max length	1	1
Median length	1	1
Mean length	1	1
Min length	1	1

Characters and Unicode

	Dataset A	Dataset B
Total characters	446	446
Distinct characters	2	2
Distinct categories	1	1 ?
Distinct scripts	1	1 ?
Distinct blocks	1	1 ?

The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

	Dataset A	Dataset B
Unique	0	0 ?
Unique (%)	0.0%	0.0%

Sample

	Dataset A	Dataset B
1st row	1	0
2nd row	0	1
3rd row	1	0
4th row	0	1
5th row	0	0

Common Values

Value	Count	Frequency (%)
0	287	64.3%
1	159	35.7%

Value	Count	Frequency (%)
0	275	61.7%
1	171	38.3%

Length

Histogram of lengths of the category

Common Values (Plot)

Dataset A

Dataset B

Value	Count	Frequency (%)
0	287	64.3%
1	159	35.7%

Value	Count	Frequency (%)
0	275	61.7%
1	171	38.3%

Most occurring characters

Value	Count	Frequency (%)
0	287	64.3%
1	159	35.7%

Value	Count	Frequency (%)
0	275	61.7%
1	171	38.3%

Most occurring categories

Value	Count	Frequency (%)
Decimal Number	446	100.0%

Value	Count	Frequency (%)
Decimal Number	446	100.0%

Most frequent character per category

Decimal Number

Value	Count	Frequency (%)
0	287	64.3%
1	159	35.7%

Value	Count	Frequency (%)
0	275	61.7%
1	171	38.3%

Most occurring scripts

Value	Count	Frequency (%)
Common	446	100.0%

Value	Count	Frequency (%)
Common	446	100.0%

Most frequent character per script

Common

Value	Count	Frequency (%)
0	287	64.3%
1	159	35.7%

Value	Count	Frequency (%)
0	275	61.7%
1	171	38.3%

Most occurring blocks

Value	Count	Frequency (%)
ASCII	446	100.0%

Value	Count	Frequency (%)
ASCII	446	100.0%

Most frequent character per block

ASCII

Value	Count	Frequency (%)
0	287	64.3%
1	159	35.7%

Value	Count	Frequency (%)
0	275	61.7%
1	171	38.3%

Pclass
Categorical

	Dataset A	Dataset B
Distinct	3	3
Distinct (%)	0.7%	0.7%
Missing	0	0
Missing (%)	0.0%	0.0%
Memory size	7.0 KiB	7.0 KiB

3	254
1	108
2	84

3	245
1	111
2	90

Length

	Dataset A	Dataset B
Max length	1	1
Median length	1	1
Mean length	1	1
Min length	1	1

Characters and Unicode

	Dataset A	Dataset B
Total characters	446	446
Distinct characters	3	3
Distinct categories	1	1 ?
Distinct scripts	1	1 ?
Distinct blocks	1	1 ?

The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

	Dataset A	Dataset B
Unique	0	0 ?
Unique (%)	0.0%	0.0%

Sample

	Dataset A	Dataset B
1st row	2	3
2nd row	3	1
3rd row	3	2
4th row	2	3
5th row	3	2

Common Values

Value	Count	Frequency (%)
3	254	57.0%
1	108	24.2%
2	84	18.8%

Value	Count	Frequency (%)
3	245	54.9%
1	111	24.9%
2	90	20.2%

Length

Histogram of lengths of the category

Common Values (Plot)

Dataset A

Dataset B

Value	Count	Frequency (%)
3	254	57.0%
1	108	24.2%
2	84	18.8%

Value	Count	Frequency (%)
3	245	54.9%
1	111	24.9%
2	90	20.2%

Most occurring characters

Value	Count	Frequency (%)
3	254	57.0%
1	108	24.2%
2	84	18.8%

Value	Count	Frequency (%)
3	245	54.9%
1	111	24.9%
2	90	20.2%

Most occurring categories

Value	Count	Frequency (%)
Decimal Number	446	100.0%

Value	Count	Frequency (%)
Decimal Number	446	100.0%

Most frequent character per category

Decimal Number

Value	Count	Frequency (%)
3	254	57.0%
1	108	24.2%
2	84	18.8%

Value	Count	Frequency (%)
3	245	54.9%
1	111	24.9%
2	90	20.2%

Most occurring scripts

Value	Count	Frequency (%)
Common	446	100.0%

Value	Count	Frequency (%)
Common	446	100.0%

Most frequent character per script

Common

Value	Count	Frequency (%)
3	254	57.0%
1	108	24.2%
2	84	18.8%

Value	Count	Frequency (%)
3	245	54.9%
1	111	24.9%
2	90	20.2%

Most occurring blocks

Value	Count	Frequency (%)
ASCII	446	100.0%

Value	Count	Frequency (%)
ASCII	446	100.0%

Most frequent character per block

ASCII

Value	Count	Frequency (%)
3	254	57.0%
1	108	24.2%
2	84	18.8%

Value	Count	Frequency (%)
3	245	54.9%
1	111	24.9%
2	90	20.2%

Name
['Text', 'Text']

	Dataset A	Dataset B
Distinct	446	446
Distinct (%)	100.0%	100.0%
Missing	0	0
Missing (%)	0.0%	0.0%
Memory size	7.0 KiB	7.0 KiB

Length

	Dataset A	Dataset B
Max length	82	82
Median length	50	50
Mean length	26.865471	26.710762
Min length	12	12

Characters and Unicode

	Dataset A	Dataset B
Total characters	11982	11913
Distinct characters	59	60
Distinct categories	7	7 ?
Distinct scripts	2	2 ?
Distinct blocks	1	1 ?

The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

	Dataset A	Dataset B
Unique	446	446 ?
Unique (%)	100.0%	100.0%

Sample

	Dataset A	Dataset B
1st row	Pinsky, Mrs. (Rosa)	Mionoff, Mr. Stoytcho
2nd row	Jensen, Mr. Svend Lauritz	Bradley, Mr. George ("George Arthur Brayton")
3rd row	Baclini, Miss. Marie Catherine	Gill, Mr. John William
4th row	Chapman, Mr. Charles Henry	de Mulder, Mr. Theodore
5th row	Naidenoff, Mr. Penko	Mitchell, Mr. Henry Michael

Value	Count	Frequency (%)
mr	269	14.8%
miss	82	4.5%
mrs	70	3.8%
john	27	1.5%
william	24	1.3%
henry	18	1.0%
master	16	0.9%
thomas	12	0.7%
edward	12	0.7%
elizabeth	11	0.6%
Other values (896)	1280	70.3%

Value	Count	Frequency (%)
mr	275	15.2%
miss	76	4.2%
mrs	67	3.7%
william	30	1.7%
john	21	1.2%
henry	18	1.0%
master	17	0.9%
edward	14	0.8%
thomas	12	0.7%
james	11	0.6%
Other values (907)	1269	70.1%

Most occurring characters

Value	Count	Frequency (%)
	1376	11.5%
r	951	7.9%
a	833	7.0%
e	829	6.9%
n	673	5.6%
s	637	5.3%
i	623	5.2%
M	565	4.7%
l	522	4.4%
o	521	4.3%
Other values (49)	4452	37.2%

Value	Count	Frequency (%)
	1365	11.5%
r	996	8.4%
e	850	7.1%
a	818	6.9%
n	646	5.4%
i	644	5.4%
s	629	5.3%
M	542	4.5%
l	536	4.5%
o	483	4.1%
Other values (50)	4404	37.0%

Most occurring categories

Value	Count	Frequency (%)
Lowercase Letter	7676	64.1%
Uppercase Letter	1828	15.3%
Space Separator	1376	11.5%
Other Punctuation	939	7.8%
Close Punctuation	78	0.7%
Open Punctuation	78	0.7%
Dash Punctuation	7	0.1%

Value	Count	Frequency (%)
Lowercase Letter	7620	64.0%
Uppercase Letter	1818	15.3%
Space Separator	1365	11.5%
Other Punctuation	950	8.0%
Close Punctuation	77	0.6%
Open Punctuation	77	0.6%
Dash Punctuation	6	0.1%

Most frequent character per category

Space Separator

Value	Count	Frequency (%)
	1376	100.0%

Value	Count	Frequency (%)
	1365	100.0%

Lowercase Letter

Value	Count	Frequency (%)
r	951	12.4%
a	833	10.9%
e	829	10.8%
n	673	8.8%
s	637	8.3%
i	623	8.1%
l	522	6.8%
o	521	6.8%
t	344	4.5%
h	260	3.4%
Other values (16)	1483	19.3%

Value	Count	Frequency (%)
r	996	13.1%
e	850	11.2%
a	818	10.7%
n	646	8.5%
i	644	8.5%
s	629	8.3%
l	536	7.0%
o	483	6.3%
t	304	4.0%
d	265	3.5%
Other values (16)	1449	19.0%

Uppercase Letter

Value	Count	Frequency (%)
M	565	30.9%
A	127	6.9%
J	116	6.3%
H	92	5.0%
S	90	4.9%
E	83	4.5%
B	82	4.5%
C	71	3.9%
W	67	3.7%
L	62	3.4%
Other values (15)	473	25.9%

Value	Count	Frequency (%)
M	542	29.8%
A	129	7.1%
J	111	6.1%
H	95	5.2%
C	92	5.1%
E	89	4.9%
S	79	4.3%
L	78	4.3%
B	72	4.0%
W	65	3.6%
Other values (15)	466	25.6%

Other Punctuation

Value	Count	Frequency (%)
.	447	47.6%
,	446	47.5%
"	42	4.5%
'	4	0.4%

Value	Count	Frequency (%)
.	446	46.9%
,	446	46.9%
"	54	5.7%
'	3	0.3%
/	1	0.1%

Close Punctuation

Value	Count	Frequency (%)
)	78	100.0%

Value	Count	Frequency (%)
)	77	100.0%

Open Punctuation

Value	Count	Frequency (%)
(	78	100.0%

Value	Count	Frequency (%)
(	77	100.0%

Dash Punctuation

Value	Count	Frequency (%)
-	7	100.0%

Value	Count	Frequency (%)
-	6	100.0%

Most occurring scripts

Value	Count	Frequency (%)
Latin	9504	79.3%
Common	2478	20.7%

Value	Count	Frequency (%)
Latin	9438	79.2%
Common	2475	20.8%

Most frequent character per script

Common

Value	Count	Frequency (%)
	1376	55.5%
.	447	18.0%
,	446	18.0%
)	78	3.1%
(	78	3.1%
"	42	1.7%
-	7	0.3%
'	4	0.2%

Value	Count	Frequency (%)
	1365	55.2%
.	446	18.0%
,	446	18.0%
)	77	3.1%
(	77	3.1%
"	54	2.2%
-	6	0.2%
'	3	0.1%
/	1	< 0.1%

Latin

Value	Count	Frequency (%)
r	951	10.0%
a	833	8.8%
e	829	8.7%
n	673	7.1%
s	637	6.7%
i	623	6.6%
M	565	5.9%
l	522	5.5%
o	521	5.5%
t	344	3.6%
Other values (41)	3006	31.6%

Value	Count	Frequency (%)
r	996	10.6%
e	850	9.0%
a	818	8.7%
n	646	6.8%
i	644	6.8%
s	629	6.7%
M	542	5.7%
l	536	5.7%
o	483	5.1%
t	304	3.2%
Other values (41)	2990	31.7%

Most occurring blocks

Value	Count	Frequency (%)
ASCII	11982	100.0%

Value	Count	Frequency (%)
ASCII	11913	100.0%

Most frequent character per block

ASCII

Value	Count	Frequency (%)
	1376	11.5%
r	951	7.9%
a	833	7.0%
e	829	6.9%
n	673	5.6%
s	637	5.3%
i	623	5.2%
M	565	4.7%
l	522	4.4%
o	521	4.3%
Other values (49)	4452	37.2%

Value	Count	Frequency (%)
	1365	11.5%
r	996	8.4%
e	850	7.1%
a	818	6.9%
n	646	5.4%
i	644	5.4%
s	629	5.3%
M	542	4.5%
l	536	4.5%
o	483	4.1%
Other values (50)	4404	37.0%

Sex
Categorical

	Dataset A	Dataset B
Distinct	2	2
Distinct (%)	0.4%	0.4%
Missing	0	0
Missing (%)	0.0%	0.0%
Memory size	7.0 KiB	7.0 KiB

male	294
female	152

male	302
female	144

Length

	Dataset A	Dataset B
Max length	6	6
Median length	4	4
Mean length	4.6816143	4.6457399
Min length	4	4

Characters and Unicode

	Dataset A	Dataset B
Total characters	2088	2072
Distinct characters	5	5
Distinct categories	1	1 ?
Distinct scripts	1	1 ?
Distinct blocks	1	1 ?

The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

	Dataset A	Dataset B
Unique	0	0 ?
Unique (%)	0.0%	0.0%

Sample

	Dataset A	Dataset B
1st row	female	male
2nd row	male	male
3rd row	female	male
4th row	male	male
5th row	male	male

Common Values

Value	Count	Frequency (%)
male	294	65.9%
female	152	34.1%

Value	Count	Frequency (%)
male	302	67.7%
female	144	32.3%

Length

Histogram of lengths of the category

Common Values (Plot)

Dataset A

Dataset B

Value	Count	Frequency (%)
male	294	65.9%
female	152	34.1%

Value	Count	Frequency (%)
male	302	67.7%
female	144	32.3%

Most occurring characters

Value	Count	Frequency (%)
e	598	28.6%
m	446	21.4%
a	446	21.4%
l	446	21.4%
f	152	7.3%

Value	Count	Frequency (%)
e	590	28.5%
m	446	21.5%
a	446	21.5%
l	446	21.5%
f	144	6.9%

Most occurring categories

Value	Count	Frequency (%)
Lowercase Letter	2088	100.0%

Value	Count	Frequency (%)
Lowercase Letter	2072	100.0%

Most frequent character per category

Lowercase Letter

Value	Count	Frequency (%)
e	598	28.6%
m	446	21.4%
a	446	21.4%
l	446	21.4%
f	152	7.3%

Value	Count	Frequency (%)
e	590	28.5%
m	446	21.5%
a	446	21.5%
l	446	21.5%
f	144	6.9%

Most occurring scripts

Value	Count	Frequency (%)
Latin	2088	100.0%

Value	Count	Frequency (%)
Latin	2072	100.0%

Most frequent character per script

Latin

Value	Count	Frequency (%)
e	598	28.6%
m	446	21.4%
a	446	21.4%
l	446	21.4%
f	152	7.3%

Value	Count	Frequency (%)
e	590	28.5%
m	446	21.5%
a	446	21.5%
l	446	21.5%
f	144	6.9%

Most occurring blocks

Value	Count	Frequency (%)
ASCII	2088	100.0%

Value	Count	Frequency (%)
ASCII	2072	100.0%

Most frequent character per block

ASCII

Value	Count	Frequency (%)
e	598	28.6%
m	446	21.4%
a	446	21.4%
l	446	21.4%
f	152	7.3%

Value	Count	Frequency (%)
e	590	28.5%
m	446	21.5%
a	446	21.5%
l	446	21.5%
f	144	6.9%

Age
Real number (ℝ)

	Dataset A	Dataset B
Distinct	76	79
Distinct (%)	21.0%	22.1%
Missing	84	88
Missing (%)	18.8%	19.7%
Infinite	0	0
Infinite (%)	0.0%	0.0%
Mean	30.072072	30.973715

	Dataset A	Dataset B
Minimum	0.42	0.42
Maximum	80	71
Zeros	0	0
Zeros (%)	0.0%	0.0%
Negative	0	0
Negative (%)	0.0%	0.0%
Memory size	7.0 KiB	7.0 KiB

Quantile statistics

	Dataset A	Dataset B
Minimum	0.42	0.42
5-th percentile	6.05	4.85
Q1	20	21
median	29	30
Q3	38	40
95-th percentile	55.95	59.15
Maximum	80	71
Range	79.58	70.58
Interquartile range (IQR)	18	19

Descriptive statistics

	Dataset A	Dataset B
Standard deviation	14.350124	14.604977
Coefficient of variation (CV)	0.47719106	0.47152809
Kurtosis	0.21040517	0.057642725
Mean	30.072072	30.973715
Median Absolute Deviation (MAD)	9	9
Skewness	0.42612785	0.33707854
Sum	10886.09	11088.59
Variance	205.92605	213.30534
Monotonicity	Not monotonic	Not monotonic

Histogram with fixed size bins (bins=50)

Value	Count	Frequency (%)
18	17	3.8%
19	15	3.4%
24	14	3.1%
22	13	2.9%
25	12	2.7%
21	11	2.5%
31	11	2.5%
27	11	2.5%
29	11	2.5%
33	10	2.2%
Other values (66)	237	53.1%
(Missing)	84	18.8%

Value	Count	Frequency (%)
36	15	3.4%
18	14	3.1%
24	13	2.9%
21	12	2.7%
30	12	2.7%
27	12	2.7%
22	12	2.7%
19	11	2.5%
31	11	2.5%
25	10	2.2%
Other values (69)	236	52.9%
(Missing)	88	19.7%

Minimum 10 values
Maximum 10 values

Value	Count	Frequency (%)
0.42	1	0.2%
0.75	1	0.2%
0.92	1	0.2%
1	3	0.7%
2	6	1.3%
3	1	0.2%
4	2	0.4%
5	2	0.4%
6	2	0.4%
7	1	0.2%

Value	Count	Frequency (%)
0.42	1	0.2%
0.75	1	0.2%
0.92	1	0.2%
1	3	0.7%
2	4	0.9%
3	3	0.7%
4	5	1.1%
5	1	0.2%
6	1	0.2%
7	3	0.7%

Value	Count	Frequency (%)
0.42	1	0.2%
0.75	1	0.2%
0.92	1	0.2%
1	3	0.7%
2	4	0.9%
3	3	0.7%
4	5	1.1%
5	1	0.2%
6	1	0.2%
7	3	0.7%

Value	Count	Frequency (%)
0.42	1	0.2%
0.75	1	0.2%
0.92	1	0.2%
1	3	0.7%
2	6	1.3%
3	1	0.2%
4	2	0.4%
5	2	0.4%
6	2	0.4%
7	1	0.2%

SibSp
Real number (ℝ)

	Dataset A	Dataset B
Distinct	7	7
Distinct (%)	1.6%	1.6%
Missing	0	0
Missing (%)	0.0%	0.0%
Infinite	0	0
Infinite (%)	0.0%	0.0%
Mean	0.57623318	0.48878924

	Dataset A	Dataset B
Minimum	0	0
Maximum	8	8
Zeros	294	306
Zeros (%)	65.9%	68.6%
Negative	0	0
Negative (%)	0.0%	0.0%
Memory size	7.0 KiB	7.0 KiB

Quantile statistics

	Dataset A	Dataset B
Minimum	0	0
5-th percentile	0	0
Q1	0	0
median	0	0
Q3	1	1
95-th percentile	3	2
Maximum	8	8
Range	8	8
Interquartile range (IQR)	1	1

Descriptive statistics

	Dataset A	Dataset B
Standard deviation	1.1346514	0.99825009
Coefficient of variation (CV)	1.9690838	2.0422915
Kurtosis	14.119952	17.217133
Mean	0.57623318	0.48878924
Median Absolute Deviation (MAD)	0	0
Skewness	3.277949	3.5165073
Sum	257	218
Variance	1.2874339	0.99650325
Monotonicity	Not monotonic	Not monotonic

Histogram with fixed size bins (bins=7)

Value	Count	Frequency (%)
0	294	65.9%
1	107	24.0%
2	18	4.0%
4	10	2.2%
3	10	2.2%
5	4	0.9%
8	3	0.7%

Value	Count	Frequency (%)
0	306	68.6%
1	106	23.8%
2	13	2.9%
4	11	2.5%
3	7	1.6%
8	2	0.4%
5	1	0.2%

Minimum 10 values
Maximum 10 values

Value	Count	Frequency (%)
0	294	65.9%
1	107	24.0%
2	18	4.0%
3	10	2.2%
4	10	2.2%
5	4	0.9%
8	3	0.7%

Value	Count	Frequency (%)
0	306	68.6%
1	106	23.8%
2	13	2.9%
3	7	1.6%
4	11	2.5%
5	1	0.2%
8	2	0.4%

Value	Count	Frequency (%)
0	306	68.6%
1	106	23.8%
2	13	2.9%
3	7	1.6%
4	11	2.5%
5	1	0.2%
8	2	0.4%

Value	Count	Frequency (%)
0	294	65.9%
1	107	24.0%
2	18	4.0%
3	10	2.2%
4	10	2.2%
5	4	0.9%
8	3	0.7%

Parch
Real number (ℝ)

	Dataset A	Dataset B
Distinct	6	6
Distinct (%)	1.3%	1.3%
Missing	0	0
Missing (%)	0.0%	0.0%
Infinite	0	0
Infinite (%)	0.0%	0.0%
Mean	0.37892377	0.35426009

	Dataset A	Dataset B
Minimum	0	0
Maximum	5	5
Zeros	334	348
Zeros (%)	74.9%	78.0%
Negative	0	0
Negative (%)	0.0%	0.0%
Memory size	7.0 KiB	7.0 KiB

Quantile statistics

	Dataset A	Dataset B
Minimum	0	0
5-th percentile	0	0
Q1	0	0
median	0	0
Q3	0.75	0
95-th percentile	2	2
Maximum	5	5
Range	5	5
Interquartile range (IQR)	0.75	0

Descriptive statistics

	Dataset A	Dataset B
Standard deviation	0.75989633	0.79609568
Coefficient of variation (CV)	2.0054069	2.2472068
Kurtosis	7.5813073	10.202235
Mean	0.37892377	0.35426009
Median Absolute Deviation (MAD)	0	0
Skewness	2.4519294	2.9011138
Sum	169	158
Variance	0.57744243	0.63376833
Monotonicity	Not monotonic	Not monotonic

Histogram with fixed size bins (bins=6)

Value	Count	Frequency (%)
0	334	74.9%
1	67	15.0%
2	38	8.5%
3	4	0.9%
5	2	0.4%
4	1	0.2%

Value	Count	Frequency (%)
0	348	78.0%
1	56	12.6%
2	33	7.4%
3	3	0.7%
5	3	0.7%
4	3	0.7%

Minimum 10 values
Maximum 10 values

Value	Count	Frequency (%)
0	334	74.9%
1	67	15.0%
2	38	8.5%
3	4	0.9%
4	1	0.2%
5	2	0.4%

Value	Count	Frequency (%)
0	348	78.0%
1	56	12.6%
2	33	7.4%
3	3	0.7%
4	3	0.7%
5	3	0.7%

Value	Count	Frequency (%)
0	348	78.0%
1	56	12.6%
2	33	7.4%
3	3	0.7%
4	3	0.7%
5	3	0.7%

Value	Count	Frequency (%)
0	334	74.9%
1	67	15.0%
2	38	8.5%
3	4	0.9%
4	1	0.2%
5	2	0.4%

Ticket
['Text', 'Text']

	Dataset A	Dataset B
Distinct	382	385
Distinct (%)	85.7%	86.3%
Missing	0	0
Missing (%)	0.0%	0.0%
Memory size	7.0 KiB	7.0 KiB

Length

	Dataset A	Dataset B
Max length	18	18
Median length	17	17
Mean length	6.7174888	6.7600897
Min length	4	3

Characters and Unicode

	Dataset A	Dataset B
Total characters	2996	3015
Distinct characters	35	32
Distinct categories	5	5 ?
Distinct scripts	2	2 ?
Distinct blocks	1	1 ?

The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

	Dataset A	Dataset B
Unique	331	336 ?
Unique (%)	74.2%	75.3%

Sample

	Dataset A	Dataset B
1st row	234604	349207
2nd row	350048	111427
3rd row	2666	233866
4th row	248731	345774
5th row	349206	C.A. 24580

Value	Count	Frequency (%)
pc	27	4.8%
c.a	16	2.8%
a/5	10	1.8%
ca	7	1.2%
2	6	1.1%
ston/o	6	1.1%
soton/o.q	4	0.7%
f.c.c	4	0.7%
w./c	4	0.7%
a/4	4	0.7%
Other values (399)	475	84.4%

Value	Count	Frequency (%)
pc	30	5.3%
c.a	17	3.0%
a/5	9	1.6%
sc/paris	7	1.2%
347082	6	1.1%
2	6	1.1%
ston/o	6	1.1%
soton/oq	5	0.9%
1601	4	0.7%
17474	3	0.5%
Other values (406)	469	83.5%

Most occurring characters

Value	Count	Frequency (%)
3	365	12.2%
1	342	11.4%
2	312	10.4%
7	243	8.1%
4	239	8.0%
6	201	6.7%
5	194	6.5%
0	191	6.4%
9	185	6.2%
8	139	4.6%
Other values (25)	585	19.5%

Value	Count	Frequency (%)
1	373	12.4%
3	372	12.3%
2	296	9.8%
7	245	8.1%
4	217	7.2%
6	203	6.7%
5	202	6.7%
0	195	6.5%
9	161	5.3%
8	142	4.7%
Other values (22)	609	20.2%

Most occurring categories

Value	Count	Frequency (%)
Decimal Number	2411	80.5%
Uppercase Letter	314	10.5%
Other Punctuation	145	4.8%
Space Separator	117	3.9%
Lowercase Letter	9	0.3%

Value	Count	Frequency (%)
Decimal Number	2406	79.8%
Uppercase Letter	338	11.2%
Other Punctuation	142	4.7%
Space Separator	116	3.8%
Lowercase Letter	13	0.4%

Most frequent character per category

Decimal Number

Value	Count	Frequency (%)
3	365	15.1%
1	342	14.2%
2	312	12.9%
7	243	10.1%
4	239	9.9%
6	201	8.3%
5	194	8.0%
0	191	7.9%
9	185	7.7%
8	139	5.8%

Value	Count	Frequency (%)
1	373	15.5%
3	372	15.5%
2	296	12.3%
7	245	10.2%
4	217	9.0%
6	203	8.4%
5	202	8.4%
0	195	8.1%
9	161	6.7%
8	142	5.9%

Space Separator

Value	Count	Frequency (%)
	117	100.0%

Value	Count	Frequency (%)
	116	100.0%

Other Punctuation

Value	Count	Frequency (%)
.	97	66.9%
/	48	33.1%

Value	Count	Frequency (%)
.	91	64.1%
/	51	35.9%

Uppercase Letter

Value	Count	Frequency (%)
C	75	23.9%
O	49	15.6%
A	44	14.0%
P	43	13.7%
S	34	10.8%
N	18	5.7%
T	17	5.4%
Q	8	2.5%
W	7	2.2%
F	6	1.9%
Other values (6)	13	4.1%

Value	Count	Frequency (%)
C	73	21.6%
O	53	15.7%
P	50	14.8%
A	41	12.1%
S	41	12.1%
N	22	6.5%
T	19	5.6%
Q	8	2.4%
I	8	2.4%
W	6	1.8%
Other values (5)	17	5.0%

Lowercase Letter

Value	Count	Frequency (%)
a	3	33.3%
s	2	22.2%
r	1	11.1%
i	1	11.1%
l	1	11.1%
e	1	11.1%

Value	Count	Frequency (%)
a	4	30.8%
r	3	23.1%
i	3	23.1%
s	3	23.1%

Most occurring scripts

Value	Count	Frequency (%)
Common	2673	89.2%
Latin	323	10.8%

Value	Count	Frequency (%)
Common	2664	88.4%
Latin	351	11.6%

Most frequent character per script

Common

Value	Count	Frequency (%)
3	365	13.7%
1	342	12.8%
2	312	11.7%
7	243	9.1%
4	239	8.9%
6	201	7.5%
5	194	7.3%
0	191	7.1%
9	185	6.9%
8	139	5.2%
Other values (3)	262	9.8%

Value	Count	Frequency (%)
1	373	14.0%
3	372	14.0%
2	296	11.1%
7	245	9.2%
4	217	8.1%
6	203	7.6%
5	202	7.6%
0	195	7.3%
9	161	6.0%
8	142	5.3%
Other values (3)	258	9.7%

Latin

Value	Count	Frequency (%)
C	75	23.2%
O	49	15.2%
A	44	13.6%
P	43	13.3%
S	34	10.5%
N	18	5.6%
T	17	5.3%
Q	8	2.5%
W	7	2.2%
F	6	1.9%
Other values (12)	22	6.8%

Value	Count	Frequency (%)
C	73	20.8%
O	53	15.1%
P	50	14.2%
A	41	11.7%
S	41	11.7%
N	22	6.3%
T	19	5.4%
Q	8	2.3%
I	8	2.3%
W	6	1.7%
Other values (9)	30	8.5%

Most occurring blocks

Value	Count	Frequency (%)
ASCII	2996	100.0%

Value	Count	Frequency (%)
ASCII	3015	100.0%

Most frequent character per block

ASCII

Value	Count	Frequency (%)
3	365	12.2%
1	342	11.4%
2	312	10.4%
7	243	8.1%
4	239	8.0%
6	201	6.7%
5	194	6.5%
0	191	6.4%
9	185	6.2%
8	139	4.6%
Other values (25)	585	19.5%

Value	Count	Frequency (%)
1	373	12.4%
3	372	12.3%
2	296	9.8%
7	245	8.1%
4	217	7.2%
6	203	6.7%
5	202	6.7%
0	195	6.5%
9	161	5.3%
8	142	4.7%
Other values (22)	609	20.2%

Fare
Real number (ℝ)

	Dataset A	Dataset B
Distinct	181	179
Distinct (%)	40.6%	40.1%
Missing	0	0
Missing (%)	0.0%	0.0%
Infinite	0	0
Infinite (%)	0.0%	0.0%
Mean	33.269721	30.389648

	Dataset A	Dataset B
Minimum	0	0
Maximum	512.3292	512.3292
Zeros	6	9
Zeros (%)	1.3%	2.0%
Negative	0	0
Negative (%)	0.0%	0.0%
Memory size	7.0 KiB	7.0 KiB

Quantile statistics

	Dataset A	Dataset B
Minimum	0	0
5-th percentile	7.129175	7.0542
Q1	7.8958	7.8958
median	14.45625	13.93125
Q3	31	30.5
95-th percentile	112.67708	106.425
Maximum	512.3292	512.3292
Range	512.3292	512.3292
Interquartile range (IQR)	23.1042	22.6042

Descriptive statistics

	Dataset A	Dataset B
Standard deviation	53.559629	45.117134
Coefficient of variation (CV)	1.6098611	1.4846218
Kurtosis	32.276095	35.222513
Mean	33.269721	30.389648
Median Absolute Deviation (MAD)	6.93335	6.63125
Skewness	4.8214596	4.7708925
Sum	14838.295	13553.783
Variance	2868.6338	2035.5558
Monotonicity	Not monotonic	Not monotonic

Histogram with fixed size bins (bins=50)

Value	Count	Frequency (%)
7.8958	27	6.1%
8.05	18	4.0%
13	16	3.6%
7.75	14	3.1%
26	14	3.1%
10.5	11	2.5%
8.6625	9	2.0%
7.925	9	2.0%
7.2292	9	2.0%
7.8542	7	1.6%
Other values (171)	312	70.0%

Value	Count	Frequency (%)
13	22	4.9%
8.05	21	4.7%
7.8958	20	4.5%
10.5	14	3.1%
26	13	2.9%
7.75	12	2.7%
7.775	10	2.2%
7.925	10	2.2%
7.2292	9	2.0%
0	9	2.0%
Other values (169)	306	68.6%

Minimum 10 values
Maximum 10 values

Value	Count	Frequency (%)
0	6	1.3%
6.2375	1	0.2%
6.4958	2	0.4%
6.75	1	0.2%
6.8583	1	0.2%
6.95	1	0.2%
6.975	1	0.2%
7.0458	1	0.2%
7.05	4	0.9%
7.0542	2	0.4%

Value	Count	Frequency (%)
0	9	2.0%
5	1	0.2%
6.2375	1	0.2%
6.4375	1	0.2%
6.45	1	0.2%
6.4958	1	0.2%
6.75	2	0.4%
6.95	1	0.2%
6.975	1	0.2%
7.0458	1	0.2%

Value	Count	Frequency (%)
0	9	2.0%
5	1	0.2%
6.2375	1	0.2%
6.4375	1	0.2%
6.45	1	0.2%
6.4958	1	0.2%
6.75	2	0.4%
6.95	1	0.2%
6.975	1	0.2%
7.0458	1	0.2%

Value	Count	Frequency (%)
0	6	1.3%
6.2375	1	0.2%
6.4958	2	0.4%
6.75	1	0.2%
6.8583	1	0.2%
6.95	1	0.2%
6.975	1	0.2%
7.0458	1	0.2%
7.05	4	0.9%
7.0542	2	0.4%

Cabin
['Text', 'Text']

	Dataset A	Dataset B
Distinct	87	88
Distinct (%)	82.9%	84.6%
Missing	341	342
Missing (%)	76.5%	76.7%
Memory size	7.0 KiB	7.0 KiB

Length

	Dataset A	Dataset B
Max length	15	11
Median length	3	3
Mean length	3.7619048	3.5384615
Min length	1	1

Characters and Unicode

	Dataset A	Dataset B
Total characters	395	368
Distinct characters	19	18
Distinct categories	3	3 ?
Distinct scripts	2	2 ?
Distinct blocks	1	1 ?

The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

	Dataset A	Dataset B
Unique	71	72 ?
Unique (%)	67.6%	69.2%

Sample

	Dataset A	Dataset B
1st row	E67	B18
2nd row	E40	C22 C26
3rd row	C92	C78
4th row	F G63	C83
5th row	D36	B77

Value	Count	Frequency (%)
c23	4	3.2%
c27	4	3.2%
c25	4	3.2%
c78	2	1.6%
c92	2	1.6%
c65	2	1.6%
b20	2	1.6%
e33	2	1.6%
c26	2	1.6%
c22	2	1.6%
Other values (87)	99	79.2%

Value	Count	Frequency (%)
b28	2	1.7%
e8	2	1.7%
b96	2	1.7%
f33	2	1.7%
c83	2	1.7%
c78	2	1.7%
c26	2	1.7%
c22	2	1.7%
c68	2	1.7%
c65	2	1.7%
Other values (87)	97	82.9%

Most occurring characters

Value	Count	Frequency (%)
C	50	12.7%
2	47	11.9%
1	38	9.6%
3	32	8.1%
6	28	7.1%
B	26	6.6%
5	22	5.6%
	20	5.1%
0	19	4.8%
D	19	4.8%
Other values (9)	94	23.8%

Value	Count	Frequency (%)
C	43	11.7%
1	37	10.1%
2	36	9.8%
B	28	7.6%
3	26	7.1%
6	26	7.1%
5	25	6.8%
8	25	6.8%
4	18	4.9%
0	17	4.6%
Other values (8)	87	23.6%

Most occurring categories

Value	Count	Frequency (%)
Decimal Number	250	63.3%
Uppercase Letter	125	31.6%
Space Separator	20	5.1%

Value	Count	Frequency (%)
Decimal Number	238	64.7%
Uppercase Letter	117	31.8%
Space Separator	13	3.5%

Most frequent character per category

Uppercase Letter

Value	Count	Frequency (%)
C	50	40.0%
B	26	20.8%
D	19	15.2%
E	15	12.0%
A	7	5.6%
F	5	4.0%
G	2	1.6%
T	1	0.8%

Value	Count	Frequency (%)
C	43	36.8%
B	28	23.9%
E	17	14.5%
D	16	13.7%
A	9	7.7%
F	3	2.6%
G	1	0.9%

Decimal Number

Value	Count	Frequency (%)
2	47	18.8%
1	38	15.2%
3	32	12.8%
6	28	11.2%
5	22	8.8%
0	19	7.6%
8	19	7.6%
7	17	6.8%
4	17	6.8%
9	11	4.4%

Value	Count	Frequency (%)
1	37	15.5%
2	36	15.1%
3	26	10.9%
6	26	10.9%
5	25	10.5%
8	25	10.5%
4	18	7.6%
0	17	7.1%
7	16	6.7%
9	12	5.0%

Space Separator

Value	Count	Frequency (%)
	20	100.0%

Value	Count	Frequency (%)
	13	100.0%

Most occurring scripts

Value	Count	Frequency (%)
Common	270	68.4%
Latin	125	31.6%

Value	Count	Frequency (%)
Common	251	68.2%
Latin	117	31.8%

Most frequent character per script

Latin

Value	Count	Frequency (%)
C	50	40.0%
B	26	20.8%
D	19	15.2%
E	15	12.0%
A	7	5.6%
F	5	4.0%
G	2	1.6%
T	1	0.8%

Value	Count	Frequency (%)
C	43	36.8%
B	28	23.9%
E	17	14.5%
D	16	13.7%
A	9	7.7%
F	3	2.6%
G	1	0.9%

Common

Value	Count	Frequency (%)
2	47	17.4%
1	38	14.1%
3	32	11.9%
6	28	10.4%
5	22	8.1%
	20	7.4%
0	19	7.0%
8	19	7.0%
7	17	6.3%
4	17	6.3%

Value	Count	Frequency (%)
1	37	14.7%
2	36	14.3%
3	26	10.4%
6	26	10.4%
5	25	10.0%
8	25	10.0%
4	18	7.2%
0	17	6.8%
7	16	6.4%
	13	5.2%

Most occurring blocks

Value	Count	Frequency (%)
ASCII	395	100.0%

Value	Count	Frequency (%)
ASCII	368	100.0%

Most frequent character per block

ASCII

Value	Count	Frequency (%)
C	50	12.7%
2	47	11.9%
1	38	9.6%
3	32	8.1%
6	28	7.1%
B	26	6.6%
5	22	5.6%
	20	5.1%
0	19	4.8%
D	19	4.8%
Other values (9)	94	23.8%

Value	Count	Frequency (%)
C	43	11.7%
1	37	10.1%
2	36	9.8%
B	28	7.6%
3	26	7.1%
6	26	7.1%
5	25	6.8%
8	25	6.8%
4	18	4.9%
0	17	4.6%
Other values (8)	87	23.6%

Embarked
Categorical

	Dataset A	Dataset B
Distinct	3	3
Distinct (%)	0.7%	0.7%
Missing	1	2
Missing (%)	0.2%	0.4%
Memory size	7.0 KiB	7.0 KiB

S	322
C	86
Q	37

S	324
C	81
Q	39

Length

	Dataset A	Dataset B
Max length	1	1
Median length	1	1
Mean length	1	1
Min length	1	1

Characters and Unicode

	Dataset A	Dataset B
Total characters	445	444
Distinct characters	3	3
Distinct categories	1	1 ?
Distinct scripts	1	1 ?
Distinct blocks	1	1 ?

The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

	Dataset A	Dataset B
Unique	0	0 ?
Unique (%)	0.0%	0.0%

Sample

	Dataset A	Dataset B
1st row	S	S
2nd row	S	S
3rd row	C	S
4th row	S	S
5th row	S	S

Common Values

Value	Count	Frequency (%)
S	322	72.2%
C	86	19.3%
Q	37	8.3%
(Missing)	1	0.2%

Value	Count	Frequency (%)
S	324	72.6%
C	81	18.2%
Q	39	8.7%
(Missing)	2	0.4%

Length

Histogram of lengths of the category

Common Values (Plot)

Dataset A

Dataset B

Value	Count	Frequency (%)
s	322	72.4%
c	86	19.3%
q	37	8.3%

Value	Count	Frequency (%)
s	324	73.0%
c	81	18.2%
q	39	8.8%

Most occurring characters

Value	Count	Frequency (%)
S	322	72.4%
C	86	19.3%
Q	37	8.3%

Value	Count	Frequency (%)
S	324	73.0%
C	81	18.2%
Q	39	8.8%

Most occurring categories

Value	Count	Frequency (%)
Uppercase Letter	445	100.0%

Value	Count	Frequency (%)
Uppercase Letter	444	100.0%

Most frequent character per category

Uppercase Letter

Value	Count	Frequency (%)
S	322	72.4%
C	86	19.3%
Q	37	8.3%

Value	Count	Frequency (%)
S	324	73.0%
C	81	18.2%
Q	39	8.8%

Most occurring scripts

Value	Count	Frequency (%)
Latin	445	100.0%

Value	Count	Frequency (%)
Latin	444	100.0%

Most frequent character per script

Latin

Value	Count	Frequency (%)
S	322	72.4%
C	86	19.3%
Q	37	8.3%

Value	Count	Frequency (%)
S	324	73.0%
C	81	18.2%
Q	39	8.8%

Most occurring blocks

Value	Count	Frequency (%)
ASCII	445	100.0%

Value	Count	Frequency (%)
ASCII	444	100.0%

Most frequent character per block

ASCII

Value	Count	Frequency (%)
S	322	72.4%
C	86	19.3%
Q	37	8.3%

Value	Count	Frequency (%)
S	324	73.0%
C	81	18.2%
Q	39	8.8%

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Heatmap
Table

Dataset A

Dataset B

Dataset A

	Age	Embarked	Fare	Parch	PassengerId	Pclass	Sex	SibSp	Survived
Age	1.000	0.000	0.151	-0.268	0.044	0.287	0.000	-0.200	0.125
Embarked	0.000	1.000	-0.082	0.054	-0.003	0.262	0.020	0.030	0.172
Fare	0.151	-0.082	1.000	0.422	-0.031	0.477	0.182	0.498	0.307
Parch	-0.268	0.054	0.422	1.000	-0.005	0.041	0.311	0.484	0.154
PassengerId	0.044	-0.003	-0.031	-0.005	1.000	0.072	0.000	-0.140	0.052
Pclass	0.287	0.262	0.477	0.041	0.072	1.000	0.144	-0.086	0.347
Sex	0.000	0.020	0.182	0.311	0.000	0.144	1.000	-0.223	0.555
SibSp	-0.200	0.030	0.498	0.484	-0.140	-0.086	-0.223	1.000	0.148
Survived	0.125	0.172	0.307	0.154	0.052	0.347	0.555	0.148	1.000

Dataset B

	Age	Embarked	Fare	Parch	PassengerId	Pclass	Sex	SibSp	Survived
Age	1.000	0.101	0.193	-0.123	0.069	0.268	0.030	-0.122	0.100
Embarked	0.101	1.000	-0.067	0.042	0.017	0.206	0.081	-0.002	0.132
Fare	0.193	-0.067	1.000	0.378	-0.040	0.494	0.190	0.460	0.298
Parch	-0.123	0.042	0.378	1.000	-0.100	0.020	0.305	0.432	0.132
PassengerId	0.069	0.017	-0.040	-0.100	1.000	0.000	0.055	-0.085	0.126
Pclass	0.268	0.206	0.494	0.020	0.000	1.000	0.163	-0.083	0.310
Sex	0.030	0.081	0.190	0.305	0.055	0.163	1.000	-0.202	0.574
SibSp	-0.122	-0.002	0.460	0.432	-0.085	-0.083	-0.202	1.000	0.198
Survived	0.100	0.132	0.298	0.132	0.126	0.310	0.574	0.198	1.000

Dataset A

A simple visualization of nullity by column.

Dataset B

A simple visualization of nullity by column.

Dataset A

Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Dataset B

Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Dataset A

The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

Dataset B

The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

First rows
Last rows

Dataset A

	PassengerId	Survived	Pclass	Name	Sex	Age	SibSp	Parch	Ticket	Fare	Cabin	Embarked
190	191	1	2	Pinsky, Mrs. (Rosa)	female	32.0	0	0	234604	13.0000	NaN	S
721	722	0	3	Jensen, Mr. Svend Lauritz	male	17.0	1	0	350048	7.0542	NaN	S
448	449	1	3	Baclini, Miss. Marie Catherine	female	5.0	2	1	2666	19.2583	NaN	C
695	696	0	2	Chapman, Mr. Charles Henry	male	52.0	0	0	248731	13.5000	NaN	S
287	288	0	3	Naidenoff, Mr. Penko	male	22.0	0	0	349206	7.8958	NaN	S
727	728	1	3	Mannion, Miss. Margareth	female	NaN	0	0	36866	7.7375	NaN	Q
317	318	0	2	Moraweck, Dr. Ernest	male	54.0	0	0	29011	14.0000	NaN	S
414	415	1	3	Sundman, Mr. Johan Julian	male	44.0	0	0	STON/O 2. 3101269	7.9250	NaN	S
558	559	1	1	Taussig, Mrs. Emil (Tillie Mandelbaum)	female	39.0	1	1	110413	79.6500	E67	S
706	707	1	2	Kelly, Mrs. Florence "Fannie"	female	45.0	0	0	223596	13.5000	NaN	S

Dataset B

	PassengerId	Survived	Pclass	Name	Sex	Age	SibSp	Parch	Ticket	Fare	Cabin	Embarked
105	106	0	3	Mionoff, Mr. Stoytcho	male	28.0	0	0	349207	7.8958	NaN	S
507	508	1	1	Bradley, Mr. George ("George Arthur Brayton")	male	NaN	0	0	111427	26.5500	NaN	S
864	865	0	2	Gill, Mr. John William	male	24.0	0	0	233866	13.0000	NaN	S
286	287	1	3	de Mulder, Mr. Theodore	male	30.0	0	0	345774	9.5000	NaN	S
672	673	0	2	Mitchell, Mr. Henry Michael	male	70.0	0	0	C.A. 24580	10.5000	NaN	S
153	154	0	3	van Billiard, Mr. Austin Blyler	male	40.5	0	2	A/5. 851	14.5000	NaN	S
329	330	1	1	Hippach, Miss. Jean Gertrude	female	16.0	0	1	111361	57.9792	B18	C
50	51	0	3	Panula, Master. Juha Niilo	male	7.0	4	1	3101295	39.6875	NaN	S
865	866	1	2	Bystrom, Mrs. (Karolina)	female	42.0	0	0	236852	13.0000	NaN	S
774	775	1	2	Hocking, Mrs. Elizabeth (Eliza Needs)	female	54.0	1	3	29105	23.0000	NaN	S

Dataset A

	PassengerId	Survived	Pclass	Name	Sex	Age	SibSp	Parch	Ticket	Fare	Cabin	Embarked
433	434	0	3	Kallio, Mr. Nikolai Erland	male	17.0	0	0	STON/O 2. 3101274	7.1250	NaN	S
482	483	0	3	Rouse, Mr. Richard Henry	male	50.0	0	0	A/5 3594	8.0500	NaN	S
804	805	1	3	Hedman, Mr. Oskar Arvid	male	27.0	0	0	347089	6.9750	NaN	S
591	592	1	1	Stephenson, Mrs. Walter Bertram (Martha Eustis)	female	52.0	1	0	36947	78.2667	D20	C
388	389	0	3	Sadlier, Mr. Matthew	male	NaN	0	0	367655	7.7292	NaN	Q
382	383	0	3	Tikkanen, Mr. Juho	male	32.0	0	0	STON/O 2. 3101293	7.9250	NaN	S
618	619	1	2	Becker, Miss. Marion Louise	female	4.0	2	1	230136	39.0000	F4	S
74	75	1	3	Bing, Mr. Lee	male	32.0	0	0	1601	56.4958	NaN	S
521	522	0	3	Vovk, Mr. Janko	male	22.0	0	0	349252	7.8958	NaN	S
544	545	0	1	Douglas, Mr. Walter Donald	male	50.0	1	0	PC 17761	106.4250	C86	C

Dataset B

	PassengerId	Survived	Pclass	Name	Sex	Age	SibSp	Parch	Ticket	Fare	Cabin	Embarked
847	848	0	3	Markoff, Mr. Marin	male	35.00	0	0	349213	7.8958	NaN	C
163	164	0	3	Calic, Mr. Jovo	male	17.00	0	0	315093	8.6625	NaN	S
221	222	0	2	Bracken, Mr. James H	male	27.00	0	0	220367	13.0000	NaN	S
803	804	1	3	Thomas, Master. Assad Alexander	male	0.42	0	1	2625	8.5167	NaN	C
237	238	1	2	Collyer, Miss. Marjorie "Lottie"	female	8.00	0	2	C.A. 31921	26.2500	NaN	S
164	165	0	3	Panula, Master. Eino Viljami	male	1.00	4	1	3101295	39.6875	NaN	S
799	800	0	3	Van Impe, Mrs. Jean Baptiste (Rosalie Paula Govaert)	female	30.00	1	1	345773	24.1500	NaN	S
187	188	1	1	Romaine, Mr. Charles Hallace ("Mr C Rolmane")	male	45.00	0	0	111428	26.5500	NaN	S
99	100	0	2	Kantor, Mr. Sinai	male	34.00	1	0	244367	26.0000	NaN	S
682	683	0	3	Olsvigen, Mr. Thor Anderson	male	20.00	0	0	6563	9.2250	NaN	S

Dataset A

	PassengerId	Survived	Pclass	Name	Sex	Age	SibSp	Parch	Ticket	Fare	Cabin	Embarked	# duplicates
Dataset does not contain duplicate rows.

Dataset B

	PassengerId	Survived	Pclass	Name	Sex	Age	SibSp	Parch	Ticket	Fare	Cabin	Embarked	# duplicates
Dataset does not contain duplicate rows.

Overview

Variables

Common Values

Length

Common Values (Plot)

Dataset A

Dataset B

Most occurring characters

Most occurring categories

Most frequent character per category

Decimal Number

Most occurring scripts

Most frequent character per script

Common

Most occurring blocks

Most frequent character per block

ASCII

Common Values

Length

Common Values (Plot)

Dataset A

Dataset B

Most occurring characters

Most occurring categories

Most frequent character per category

Decimal Number

Most occurring scripts

Most frequent character per script

Common

Most occurring blocks

Most frequent character per block

ASCII

Most occurring characters

Most occurring categories

Most frequent character per category

Space Separator

Lowercase Letter

Uppercase Letter

Other Punctuation

Close Punctuation

Open Punctuation

Dash Punctuation

Most occurring scripts

Most frequent character per script

Common

Latin

Most occurring blocks

Most frequent character per block

ASCII

Common Values

Length

Common Values (Plot)

Dataset A

Dataset B

Most occurring characters

Most occurring categories

Most frequent character per category

Lowercase Letter

Most occurring scripts

Most frequent character per script

Latin

Most occurring blocks

Most frequent character per block

ASCII

Most occurring characters

Most occurring categories

Most frequent character per category

Decimal Number

Space Separator

Other Punctuation

Uppercase Letter

Lowercase Letter

Most occurring scripts

Most frequent character per script

Common

Latin

Most occurring blocks

Most frequent character per block

ASCII

Most occurring characters